Some searches may not work properly. We apologize for the inconvenience.
My bibliography Save this articleBig data forecasting of South African inflation
Author
Abstract
Suggested Citation
DOI: 10.1007/s00181-022-02329-y
Download full text from publisher
As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.
Other versions of this item:
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
References listed on IDEAS
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021.
"Economic Predictions With Big Data: The Illusion of Sparsity,"
Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021. "Economic predictions with big data: the illusion of sparsity," Working Paper Series 2542, European Central Bank.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic Predictions with Big Data: The Illusion of Sparsity," Liberty Street Economics 20180521, Federal Reserve Bank of New York.
- Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
- Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
- Geoffrey Woglom, 2005. "Forecasting South African Inflation," South African Journal of Economics, Economic Society of South Africa, vol. 73(2), pages 302-320, June.
- Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
- Jeff Fuhrer & George Moore, 1995.
"Inflation Persistence,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 127-159.
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Finance and Economics Discussion Series 93-17, Board of Governors of the Federal Reserve System (U.S.).
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Stan Plessis & Gideon Rand & Kevin Kotzé, 2015.
"Measuring Core Inflation in South Africa,"
South African Journal of Economics, Economic Society of South Africa, vol. 83(4), pages 527-548, December.
- Gideon Du Rand & Kevin Kotze & Stan Du Plessis, 2015. "Measuring Core Inflation in South Africa," Working Papers 503, Economic Research Southern Africa.
- Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2021.
"Nowcasting ‘True’ Monthly U.S. Gdp During The Pandemic,"
National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 44-70, April.
- Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2021. "Nowcasting 'true' monthly US GDP during the pandemic," CAMA Working Papers 2021-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
- P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011.
"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls,"
Papers
1201.0224, arXiv.org, revised May 2012.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 26/13, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 10/12, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP10/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP26/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
- Emi Nakamura & Jón Steinsson, 2013.
"Price Rigidity: Microeconomic Evidence and Macroeconomic Implications,"
Annual Review of Economics, Annual Reviews, vol. 5(1), pages 133-163, May.
- Emi Nakamura & Jón Steinsson, 2013. "Price Rigidity: Microeconomic Evidence and Macroeconomic Implications," NBER Working Papers 18705, National Bureau of Economic Research, Inc.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Wolters Maik H. & Tillmann Peter, 2015.
"The changing dynamics of US inflation persistence: a quantile regression approach,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 161-182, April.
- Peter Tillmann & Maik H. Wolters, 2012. "The changing dynamics of US inflation persistence: a quantile regression approach," MAGKS Papers on Economics 201206, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Economics Working Papers 2014-09, Christian-Albrechts-University of Kiel, Department of Economics.
- Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Kiel Working Papers 1951, Kiel Institute for the World Economy (IfW Kiel).
- Tillmann, Peter & Wolters, Maik Hendrik, 2012. "The changing dynamics of US inflation persistence: A quantile regression approach," IMFS Working Paper Series 60, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
- Mehmet Balcilar & Rangan Gupta & Kevin Kotzé, 2017.
"Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model,"
Empirical Economics, Springer, vol. 53(1), pages 117-135, August.
- Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2016. "Forecasting South African Macroeconomic Variables with a Markov-Switching Small Open-Economy Dynamic Stochastic General Equilibrium Model," Working Papers 201603, University of Pretoria, Department of Economics.
- Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2016. "Forecasting South African Macroeconomic Variables with a Markov-Switching Small Open-Economy Dynamic Stochastic General Equilibrium Model," School of Economics Macroeconomic Discussion Paper Series 2016-05, School of Economics, University of Cape Town.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013.
"Complete subset regressions,"
Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," University of California at San Diego, Economics Working Paper Series qt1st3n7z7, Department of Economics, UC San Diego.
- Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010.
"Large Bayesian vector auto regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
- Marta Bańbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92, January.
- Reichlin, Lucrezia & Giannone, Domenico & Banbura, Marta, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
- Domenico Giannone & Martha Banbura & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," ULB Institutional Repository 2013/13388, ULB -- Universite Libre de Bruxelles.
- Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
- Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Schmitt-Grohe, Stephanie & Uribe, Martin, 2004.
"Solving dynamic general equilibrium models using a second-order approximation to the policy function,"
Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
- Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," Departmental Working Papers 200106, Rutgers University, Department of Economics.
- Uribe, MartÃn & Schmitt-Grohé, Stephanie, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
- Stephanie Schmitt-Grohe & Martin Uribe, 2002. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," NBER Technical Working Papers 0282, National Bureau of Economic Research, Inc.
- Ba M. Chu & Kim Huynh & David T. Jacho-Chávez & Oleksiy Kryvtsov, 2018. "On the Evolution of the United Kingdom Price Distributions," Staff Working Papers 18-25, Bank of Canada.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014.
"High-Dimensional Methods and Inference on Structural and Treatment Effects,"
Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
- Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021.
"Can Machine Learning Catch The Covid-19 Recession?,"
National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, April.
- Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021. "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers 15867, C.E.P.R. Discussion Papers.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," CIRANO Working Papers 2021s-09, CIRANO.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Working Papers 21-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.
- A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.
- Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
- Litterman, Robert B, 1986.
"Forecasting with Bayesian Vector Autoregressions-Five Years of Experience,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
- Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
- Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016.
"Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation,"
Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
- Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 15-19, Eastern Mediterranean University, Department of Economics.
- Patrick T. kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.
- Klenow, Peter J. & Malin, Benjamin A., 2010.
"Microeconomic Evidence on Price-Setting,"
Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 6, pages 231-284,
Elsevier.
- Peter J. Klenow & Benjamin A. Malin, 2010. "Microeconomic Evidence on Price-Setting," NBER Working Papers 15826, National Bureau of Economic Research, Inc.
- Daleen Smal & Coen Pretorius & Nelene Ehlers, 2007. "The core forecasting model of the South African Reserve Bank," Working Papers 3195, South African Reserve Bank.
- Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
- James H. Stock & Mark W. Watson, 2010.
"Modeling inflation after the crisis,"
Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
- James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," NBER Working Papers 16488, National Bureau of Economic Research, Inc.
- Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using micropricelevel data South Africas pricing dynamics," Working Papers 7353, South African Reserve Bank.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000.
"The Generalized Dynamic-Factor Model: Identification And Estimation,"
The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
- A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017.
"Program Evaluation and Causal Inference With High‐Dimensional Data,"
Econometrica, Econometric Society, vol. 85, pages 233-298, January.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers CWP13/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020.
"Tracking the COVID-19 Crisis with High-Resolution Transaction Data,"
Cambridge Working Papers in Economics
2030, Faculty of Economics, University of Cambridge.
- Hansen, Stephen & Carvalho, Vasco & GarcÃa, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & RodrÃguez Mora, José V & Ruiz, Pep, 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," CEPR Discussion Papers 14642, C.E.P.R. Discussion Papers.
- Franz Ruch & Mehmet Balcilar & Rangan Gupta & Mampho P. Modise, 2020.
"Forecasting core inflation: the case of South Africa,"
Applied Economics, Taylor & Francis Journals, vol. 52(28), pages 3004-3022, June.
- Franz Ruch & Mehmet Balcilar Author-Name-First Mehmet & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 15-08, Eastern Mediterranean University, Department of Economics.
- Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
- Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024.
"The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
- Raj Chetty & John N. Friedman & Michael Stepner & The Opportunity Insights Team, 2020. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," NBER Working Papers 27431, National Bureau of Economic Research, Inc.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Gupta, Rangan & Kabundi, Alain, 2011.
"A large factor model for forecasting macroeconomic variables in South Africa,"
International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
- Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- Petrella, Ivan & Santoro, Emiliano & Simonsen, Lasse de la Porte, 2018.
"Time-varying Price Flexibility and Inflation Dynamics,"
CEPR Discussion Papers
13027, C.E.P.R. Discussion Papers.
- Petrella, Ivan & Santoro, Emiliano & Simonsen, Lasse P., 2019. "Time-varying Price Flexibility and Inflation Dynamics," EMF Research Papers 28, Economic Modelling and Forecasting Group.
- Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
- Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Mr Steinbach & Pt Mathuloe & Bw Smit, 2009.
"An Open Economy New Keynesian Dsge Model Of The South African Economy,"
South African Journal of Economics, Economic Society of South Africa, vol. 77(2), pages 207-227, June.
- Rudi Steinbach & Patience Mathuloe & Ben Smit, 2009. "An open economy New Keynesian DSGE model of the South African economy," Working Papers 3431, South African Reserve Bank.
- Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
- David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.
- Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
- James H. Stock & Mark W. Watson, 2020. "Slack and Cyclically Sensitive Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(S2), pages 393-428, December.
- Sami Alpanda & Kevin Kotzé & Geoffrey Woglom, 2010. "The Role Of The Exchange Rate In A New Keynesian Dsge Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 78(2), pages 170-191, June.
- Scott R Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis & Jeffrey Pontiff, 0.
"How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic,"
The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 834-862.
- Scott R. Baker & R.A. Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2020. "How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic," NBER Working Papers 26949, National Bureau of Economic Research, Inc.
- Scott R. Baker & R.A. Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2020. "How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic," Working Papers 2020-30, Becker Friedman Institute for Research In Economics.
- Rajashri Chakrabarti & Sebastian Heise & Davide Melcangi & Maxim L. Pinkovskiy & Giorgio Topa, 2020. "Did State Reopenings Increase Consumer Spending?," Liberty Street Economics 20200918b, Federal Reserve Bank of New York.
- Kenneth Creamer & Greg Farrell & Neil Rankin, 2012.
"What Price-Level Data Can Tell Us About Pricing Conduct In South Africa,"
South African Journal of Economics, Economic Society of South Africa, vol. 80(4), pages 490-509, December.
- Dr. Kenneth Creamer & Dr. Greg Farrell & Prof. Neil Rankin, 2012. "What pricelevel data can tell us about pricing conduct in South Africa," Working Papers 5117, South African Reserve Bank.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Marta Bańbura, 2008.
"Large Bayesian VARs,"
2008 Meeting Papers
334, Society for Economic Dynamics.
- Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta, 2008. "Large Bayesian VARs," Working Paper Series 966, European Central Bank.
- Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
- Litterman, Robert B, 1986. "A Statistical Approach to Economic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 1-4, January.
- Christian Schumacher, 2007.
"Forecasting German GDP using alternative factor models based on large datasets,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
- Schumacher, Christian, 2005. "Forecasting German GDP using alternative factor models based on large datasets," Discussion Paper Series 1: Economic Studies 2005,24, Deutsche Bundesbank.
- Geoffrey Woglom & Kevin Kotze & Sami Alpanda, 2010. "Should Central Banks of Small Open Economies Respond to Exchange Rate Fluctuations: The Case of South Africa," Working Papers 174, Economic Research Southern Africa.
- Byron Botha & Shaun de Jager & Franz Ruch & Rudi Steinbach, 2017. "The Quarterly Projection Model of the SARB," Working Papers 8000, South African Reserve Bank.
- Rangan Gupta & Alain Kabundi, 2010.
"Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
- Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models," Working Papers 200830, University of Pretoria, Department of Economics.
- Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
- Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, May.
- Fuhrer, Jeffrey C., 2010.
"Inflation Persistence,"
Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 9, pages 423-486,
Elsevier.
- Jeff Fuhrer & George Moore, 1995. "Inflation Persistence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 127-159.
- Jeffrey C. Fuhrer, 2009. "Inflation persistence," Working Papers 09-14, Federal Reserve Bank of Boston.
- Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "The Value Of Robust Statistical Forecasts In The Covid-19 Pandemic," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 19-43, April.
- Guangling 'Dave' Liu & Rangan Gupta & Eric Schaling, 2009.
"A New-Keynesian DSGE model for forecasting the South African economy,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 387-404.
- Guangling (Dave) Liu & Rangan Gupta & Eric Schaling, 2008. "A New-Keynesian DSGE Model for Forecasting the South African Economy," Working Papers 200805, University of Pretoria, Department of Economics.
- Cornelia Hammer & Ms. Diane C Kostroch & Mr. Gabriel Quiros-Romero, 2017. "Big Data: Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 2017/006, International Monetary Fund.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- World Bank, 2014. "Central America : Big Data in Action for Development," World Bank Publications - Reports 21325, The World Bank Group.
- Sami Alpanda & Kevin Kotzé & Geoffrey Woglom, 2011. "Forecasting Performance Of An Estimated Dsge Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 79(1), pages 50-67, March.
- Shelby R. Buckman & Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, 2020. "News Sentiment in the Time of COVID-19," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(08), pages 1-05, April.
- Jean Boivin & Serena Ng, 2005.
"Understanding and Comparing Factor-Based Forecasts,"
International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
- Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
- Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
- Jens Mehrhoff, 2017. "Central banks' use of and interest in "big data"," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Big Data, volume 44, Bank for International Settlements.
- Rajashri Chakrabarti & Sebastian Heise & Davide Melcangi & Maxim L. Pinkovskiy & Giorgio Topa, 2020. "How Did State Reopenings Affect Small Businesses?," Liberty Street Economics 20200921, Federal Reserve Bank of New York.
- Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using microprice data Stickyprice inflation," Working Papers 7354, South African Reserve Bank.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
- Okiriza Wibisono & Hidayah Dhini Ari & Anggraini Widjanarti & Alvin Andhika Zulen & Bruno Tissot, 2019. "The use of big data analytics and artificial intelligence in central banking – An overview," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021.
"Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
- Marcelo Madeiros & Gabriel Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2019. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Working Papers Central Bank of Chile 834, Central Bank of Chile.
- Rajashri Chakrabarti & Maxim L. Pinkovskiy, 2020. "Did State Reopenings Increase Social Interactions?," Liberty Street Economics 20200617, Federal Reserve Bank of New York.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
- Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, October.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
- Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.
- Cornelia Hammer & Diane C Kostroch & Gabriel Quiros-Romero, 2017. "Big Data; Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 17/06, International Monetary Fund.
- Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
- Alberto Cavallo, 2024.
"Inflation with Covid Consumption Baskets,"
IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(2), pages 902-917, June.
- Alberto Cavallo, 2020. "Inflation with Covid Consumption Baskets," NBER Working Papers 27352, National Bureau of Economic Research, Inc.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.
- Bruno Tissot, 2019. "Big data for central banks," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
- Peter McCullagh & Nicholas G Polson, 2018. "Statistical sparsity," Biometrika, Biometrika Trust, vol. 105(4), pages 797-814.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023.
"Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany,"
Discussion Papers
34/2023, Deutsche Bundesbank.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
- Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019.
"Macroeconomic forecast accuracy in a data‐rich environment,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
- Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
- Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021.
"Macroeconomic data transformations matter,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Working Papers 20-17, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Mar 2021.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2020. "Macroeconomic Data Transformations Matter," CIRANO Working Papers 2020s-42, CIRANO.
- Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
- Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
- Luciani, Matteo, 2014.
"Forecasting with approximate dynamic factor models: The role of non-pervasive shocks,"
International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
- Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
- Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
- Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022.
"Forecasting: theory and practice,"
International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
- Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
- Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
- repec:ipg:wpaper:2014-562 is not listed on IDEAS
- Dimitris Korobilis, 2018.
"Machine Learning Macroeconometrics: A Primer,"
Working Paper series
18-30, Rimini Centre for Economic Analysis.
- Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.
- Raul Ibarra & Luis M. Gomez-Zamudio, 2017.
"Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico,"
Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 173-203, April.
- Ibarra-Ramírez Raúl & Gómez-Zamudio Luis M., 2017. "Are daily financial data useful for forecasting GDP? Evidence from Mexico," Working Papers 2017-17, Banco de México.
- Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023.
"Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks,"
International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers 2011.07920, arXiv.org, revised Feb 2022.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print emse-04624940, HAL.
- Rangan Gupta & Patrick T. kanda & Mampho P. Modise & Alessia Paccagnini, 2013.
"DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa,"
Working Papers
201374, University of Pretoria, Department of Economics.
- Rangan Gupta & Patrick Kanda & Mampho Modise & Alessia Paccagnini, 2013. "DGSE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers 259, University of Milano-Bicocca, Department of Economics, revised Nov 2013.
- Rangan Gupta & Patrick T. Kanda & Mampho P. Modise & Alessia Paccagnini, 2015.
"DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa,"
Applied Economics, Taylor & Francis Journals, vol. 47(3), pages 207-221, January.
- Rangan Gupta & Patrick T. kanda & Mampho P. Modise & Alessia Paccagnini, 2013. "DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers 201374, University of Pretoria, Department of Economics.
- Rangan Gupta & Patrick T. Kanda & Mampho P. Modise & Alessia Paccagnini, 2015. "DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa," Open Access publications 10197/7351, School of Economics, University College Dublin.
- Gupta, Rangan & Kabundi, Alain, 2011.
"A large factor model for forecasting macroeconomic variables in South Africa,"
International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
- Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
- Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
- Mu-Chun Wang, 2009.
"Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 167-182.
- Wang, Mu-Chun, 2008. "Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment," Discussion Paper Series 1: Economic Studies 2008,04, Deutsche Bundesbank.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- Smeekes, Stephan & Wijler, Etienne, 2018.
"Macroeconomic forecasting using penalized regression methods,"
International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
- Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
- Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023.
"Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models,"
Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
- Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
More about this item
Keywords
Micro-data; Inflation; High-dimensional regression; Penalised likelihood; Bayesian methods; Statistical learning;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:65:y:2023:i:1:d:10.1007_s00181-022-02329-y. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.